DEFORMATION OF VISCOELASITIC DROPLETS THROUGH INERTIAL FOCUSING IN MICROFLUIDICS

Inertial focusing in microfluidics has been a promising method for cell sorting in recent years. Despite various experiments and applications in devices implemented for cell sorting, the mechanisms of inertial focusing of deformable particles have yet to be elucidated. Various experiments conducted in inertial focusing demonstrated that the shape and deformation of droplets would affect particle motion and their steady state focusing position. The significance of deformation in inertial focusing inspired this work. In this thesis, we would show the deformation of viscoelastic droplets under different flow rates. Our experimental results show that for dimensionless droplets size 0.2-0.4, an oval shape is formed and a simple two-dimensional measurement has been used to define a deformation of viscoelastic droplets. Analysis shows that the deformation by Taylor measurement could be estimated by the flow and droplet properties and an equation for deformation prediction would be presented. As deformation is an intrinsic factor to any deformable particle in inertial focusing, this connection would provide convenience in assessing deformation contributions in affecting focusing positions. We envision a better understanding of the mechanics of inertial focusing, leading to the improvement of microfluidic devices for cell sorting

Advances in multi-modal non-invasive imaging techniques in the diagnosis and treatment of polypoidal choroidal vasculopathy

Polypoidal choroidal vasculopathy (PCV) is a disease characterized by subretinal pigment epithelium (RPE) orange-red polypoidal lesions and abnormal branching neovascular networks (BNNs). In recent years, various non-invasive imaging technologies have rapidly developed, especially the emergence of optical coherence tomography angiography (OCTA), multi-spectral imaging, and other technologies, which enable the observation of more features of PCV. In addition, these technologies are faster and less invasive compared to indocyanine green angiography (ICGA). Multi-modal imaging, which combined multiple imaging techniques, provides important references for the diagnosis and treatment of PCV with the assistance of regression models, deep learning, and other algorithms. In this study, we reviewed the non-invasive imaging techniques, multi-modal imaging diagnosis, and multi-scene therapeutic applications of PCV, with the aim of providing a reference for non-invasive multi-modal diagnosis and treatment of PCV

Optron: Better Medical Image Registration via Optimizing in the Loop

Previously, in the field of image registration, there are mainly two paradigms, the traditional optimization-based methods, and the deep-learning-based methods. We designed a robust training architecture that is simple and generalizable. We present Optron, a general training architecture incorporating the idea of optimizing-in-the-loop. By iteratively optimizing the prediction result of a deep learning model through a plug-and-play optimizer module in the training loop, Optron introduces pseudo ground truth to an unsupervised training process. This pseudo supervision provides more direct guidance towards model training compared with unsupervised methods. Utilizing this advantage, Optron can consistently improve the models' performance and convergence speed. We evaluated our method on various combinations of models and datasets, and we have achieved state-of-the-art performance on the IXI dataset, improving the previous state-of-the-art method TransMorph by a significant margin of +1.6% DSC. Moreover, Optron also consistently achieved positive results with other models and datasets. It increases the validation DSC on IXI for VoxelMorph and ViT-V-Net by +2.3% and +2.2% respectively, demonstrating our method's generalizability. Our implementation is publicly available at https://github.com/miraclefactory/optronComment: 10 pages, 5 figures, 4 table

Giant photoinduced lattice distortion in oxygen-vacancy ordered SrCoO2.5 thin films

Despite of the tremendous efforts spent on the oxygen vacancy migration in determining the property optimization of oxygen-vacancy enrichment transition metal oxides, few has focused on their dynamic behaviors non-equilibrium states. In this work, we performed multi-timescale ultrafast X-ray diffraction measurements by using picosecond synchrotron X-ray pulses and femtosecond table-top X-ray pulses to monitor the structural dynamics in the oxygen-vacancy ordered SrCoO2.5 thin films. A giant photoinduced strain ({\Delta}c/c > 1%) was observed, whose distinct correlation with the pump photon energy indicates a non-thermal origin of the photoinduced strain. The sub-picosecond resolution X-ray diffraction reveals the formation and propagation of the coherent acoustic phonons inside the film. We also simulate the effect of photoexcited electron-hole pairs and the resulting lattice changes using the Density Function Theory method to obtain further insight on the microscopic mechanism of the measured photostriction effect. Comparable photostrictive responses and the strong dependence on excitation wavelength are predicted, revealing a bonding to anti-bonding charge transfer or high spin to intermediate spin crossover induced lattice expansion in the oxygen-vacancy films.Comment: 12 pages, 4 figures, support materia

Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation

Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smoothing issue. The performance of graph neural networks decays fast as the number of stacked layers increases, and the Dirichlet energy associated with the graph decreases to zero as well. In this work, we introduce a framelet system into the analysis of Dirichlet energy and take a multi-scale perspective to leverage the Dirichlet energy and alleviate the over-smoothing issue. Specifically, we develop a Framelet Augmentation strategy by adjusting the update rules with positive and negative increments for low-pass and high-passes respectively. Based on that, we design the Energy Enhanced Convolution (EEConv), which is an effective and practical operation that is proved to strictly enhance Dirichlet energy. From a message-passing perspective, EEConv inherits multi-hop aggregation property from the framelet transform and takes into account all hops in the multi-scale representation, which benefits the node classification tasks over heterophilous graphs. Experiments show that deep GNNs with EEConv achieve state-of-the-art performance over various node classification datasets, especially for heterophilous graphs, while also lifting the Dirichlet energy as the network goes deeper

Nanoscale Bandgap Tuning across an Inhomogeneous Ferroelectric Interface

We report nanoscale bandgap engineering via a local strain across the inhomogeneous ferroelectric interface, which is controlled by the visible-light-excited probe voltage. Switchable photovolatic effects and the spectral response of the photocurrent were explore to illustrate the reversible bandgap variation (~0.3eV). This local-strain-engineered bandgap has been further revealed by in situ probe-voltage-assisted valence electron energy-loss spectroscopy (EELS). Phase-field simulations and first-principle calculations were also employed for illustration of the large local strain and the bandgap variation in ferroelectric perovskite oxides. This reversible bandgap tuning in complex oxides demonstrates a framework for the understanding of the opticallyrelated behaviors (photovoltaic, photoemission, and photocatalyst effects) affected by order parameters such as charge, orbital, and lattice parameters

Mutations in an Atypical TIR-NB-LRR-LIM Resistance Protein Confer Autoimmunity

In order to defend against microbial infection, plants employ a complex immune system that relies partly on resistance (R) proteins that initiate intricate signaling cascades upon pathogen detection. The resistance signaling network utilized by plants is only partially characterized. A genetic screen conducted to identify novel defense regulators involved in this network resulted in the isolation of the snc6-1D mutant. Positional cloning revealed that this mutant contained a molecular lesion in the chilling sensitive 3 (CHS3) gene, thus the allele was renamed chs3-2D. CHS3 encodes a TIR-NB-LRR R protein that contains a C-terminal zinc-binding LIM (Lin-11, Isl-1, Mec-3) domain. Although this protein has been previously implicated in cold stress and defense response, the role of the LIM domain in modulating protein activity is unclear. The chs3-2D allele contains a G to A point mutation causing a C1340 to Y1340 substitution close to the LIM domain. It encodes a dominant gain-of-function mutation. The chs3-2D mutant is severely stunted and displays curled leaf morphology. Additionally, it constitutively expresses PATHOGENESIS-RELATED (PR) genes, accumulates salicylic acid, and shows enhanced resistance to the virulent oomycete isolate Hyaloperonospora arabidopsidis (H.a.) Noco2. Subcellular localization assays using GFP fusion constructs indicate that both CHS3 and chs3-2D localize to the nucleus. A third chs3 mutant allele, chs3-3D, was identified in an unrelated genetic screen in our lab. This allele contains a C to T point mutation resulting in an M1017 to V1017 substitution in the LRR–LIM linker region. Additionally, a chs3-2D suppressor screen identified two revertant alleles containing secondary mutations that abolish the mutant morphology. Analysis of the locations of these molecular lesions provides support for the hypothesis that the LIM domain represses CHS3 R-like protein activity. This repression may occur through either autoinhibition or binding of a negative defense regulator

Health literacy and use and trust in health information

This is a post-print of an article whose final version has been published in Journal of Health Communication, Taylor and Francis, 2018.There is a need to investigate which health information sources are used and trusted by people with limited health literacy to help identify strategies for addressing knowledge gaps that can contribute to preventable illness. We examined whether health literacy was associated with people?s use of and trust in a range of potential health information sources. Six hundred participants from a GfK Internet survey panel completed an online survey. We assessed health literacy using the Newest Vital Sign, the sources participants used to get health information, and the extent to which participants trusted health information from these sources. We performed multivariable regressions, controlling for demographic characteristics. Lower health literacy was associated with lower odds of using medical websites for health information and with higher odds of using television, social media, and blogs or celebrity webpages. People with lower health literacy were less likely to trust health information from specialist doctors and dentists, but more likely to trust television, social media, blogs/celebrity webpages, friends, and pharmaceutical companies. People with limited health literacy had higher rates of using and trusting sources such as social media and blogs, which might contain lower quality health information compared to information from healthcare professionals. Thus, it might be necessary to enhance the public's ability to evaluate the quality of health information sources. The results of this study could be used to improve the reach of high quality health information among people with limited health literacy and thereby increase the effectiveness of health communication programs and campaigns.Peer reviewedCommunity Health Sciences, Counseling and Counseling Psycholog